In most studies, the emerged language structures are analyzed for shared commonalities with natural languages, such as compositionality or encoding efficiency. Although desired, it is nontrivial for such properties to emerge spontaneously [1]}. As a result, researchers introduce pressures during training and/or structure the learning process to incentivise specific language properties. To encourage compositionality in the emergent communication protocol, one can introduce populations of agents [2]}, [3]} or the need for a language to be easily teachable [4]}. Efficient communication can be incentivised by modifying the agents' reward structure, e.g., by adding an internal cost of articulation [5]} or by prioritizing messages based on a metric of confidence [6]}. Sparse communication has also been shown to emerge in mixed and competitive settings, where communicating too much might harm agent performance, e.g., in the case of prey coordinating an escape from predators [7]}. In parallel, researchers have studied communication patterns during competitive games—e.g., auctions—where the capacity of the communication channel is externally bounded [8]}. Here, we propose situating the communication in a multi-step task as a mechanism to shape the properties of the emergent communication protocol.
